Machine Learning in Igneous Petrology and Volcanology: State-of-the-Art and Perspectives

The work reports on the state-of-the-art and future perspectives of Machine Learning (ML) in igneous petrology and volcanology. To do that, it starts reviewing established methods that mainly concern clustering, dimensionality reduction, classification, and regression. Among them, clustering and dimensionality reduction are particularly valuable for decoding the chemical record stored in igneous and metamorphic phases and to enhance data visualization, respectively. Classification and regression tasks find applications, for example, in petrotectonic discrimination and geothermobarometry, respectively. The main core of the discussion will consist of depicting the next future for ML in petrological and volcanological applications. I propose a future scenario where ML methods will progressively integrate and support established petrological and volcanological methods in boosting new findings, possibly providing a paradigm shift. In this framework, the use of multimodal data, data fusion, physics-informed neural networks, and ML-supported numerical simulations, will play a significant role. Also, the use of ML hypotheses formulation and symbolic regression could significantly boost new findings. In the proposed scenario, the main challenges are: a) progressively link machine learning algorithms with the physical and thermodynamic nature of the investigated processes, b) make deep learning algorithms more transparent, as they often operate as “black boxes,” c) advance in exploring cutting edge tools that rise from researches in Artificial Intelligence, and overall, d) start a collaborative effort among researchers coming from different disciplines in research and teaching.